def main(_):
seed = common.set_random_seed(FLAGS.random_seed)
gin_file = common.get_gin_file()
gin.parse_config_files_and_bindings(gin_file, FLAGS.gin_param)
algorithm_ctor = gin.query_parameter(
'TrainerConfig.algorithm_ctor').scoped_configurable_fn
env = create_environment(nonparallel=True, seed=seed)
env.reset()
common.set_global_env(env)
config = policy_trainer.TrainerConfig(root_dir="")
data_transformer = create_data_transformer(config.data_transformer_ctor,
env.observation_spec())
config.data_transformer = data_transformer
observation_spec = data_transformer.transformed_observation_spec
common.set_transformed_observation_spec(observation_spec)
algorithm = algorithm_ctor(
observation_spec=observation_spec,
action_spec=env.action_spec(),
config=config)
try:
policy_trainer.play(
FLAGS.root_dir,
env,
algorithm,
checkpoint_step=FLAGS.checkpoint_step or "latest",
epsilon_greedy=FLAGS.epsilon_greedy,
num_episodes=FLAGS.num_episodes,
max_episode_length=FLAGS.max_episode_length,
sleep_time_per_step=FLAGS.sleep_time_per_step,
record_file=FLAGS.record_file,
ignored_parameter_prefixes=FLAGS.ignored_parameter_prefixes.split(
",") if FLAGS.ignored_parameter_prefixes else [])
finally:
env.close()
def play(root_dir, algorithm_ctor):
"""Play using the latest checkpoint under `train_dir`.
Args:
root_dir (str): directory where checkpoints stores
algorithm_ctor (Callable): callable that create an algorithm
parameter value is bind with `Trainer.algorithm_ctor`,
just config `Trainer.algorithm_ctor` when using with gin configuration
"""
env = create_environment(num_parallel_environments=1)
algorithm = algorithm_ctor(env)
policy_trainer.play(root_dir, env, algorithm)
def main(_):
gin_file = common.get_gin_file()
gin.parse_config_files_and_bindings(gin_file, FLAGS.gin_param)
algorithm_ctor = gin.query_parameter('TrainerConfig.algorithm_ctor')
env = create_environment(num_parallel_environments=1)
algorithm = algorithm_ctor(env)
policy_trainer.play(FLAGS.root_dir,
env,
algorithm,
checkpoint_name=FLAGS.checkpoint_name,
greedy_predict=FLAGS.greedy_predict,
random_seed=FLAGS.random_seed,
num_episodes=FLAGS.num_episodes,
sleep_time_per_step=FLAGS.sleep_time_per_step,
record_file=FLAGS.record_file)
def main(_):
seed = common.set_random_seed(FLAGS.random_seed,
not FLAGS.use_tf_functions)
gin_file = common.get_gin_file()
gin.parse_config_files_and_bindings(gin_file, FLAGS.gin_param)
algorithm_ctor = gin.query_parameter(
'TrainerConfig.algorithm_ctor').scoped_configurable_fn
env = create_environment(nonparallel=True, seed=seed)
env.reset()
common.set_global_env(env)
algorithm = algorithm_ctor(observation_spec=env.observation_spec(),
action_spec=env.action_spec())
policy_trainer.play(FLAGS.root_dir,
env,
algorithm,
checkpoint_name=FLAGS.checkpoint_name,
epsilon_greedy=FLAGS.epsilon_greedy,
num_episodes=FLAGS.num_episodes,
sleep_time_per_step=FLAGS.sleep_time_per_step,
record_file=FLAGS.record_file,
use_tf_functions=FLAGS.use_tf_functions)
env.pyenv.close()
